feat: LLM backbone encoder for JEPA training with local model selection

Add support for using open-weights LLMs (Llama, Qwen, Gemma, Mistral, Phi)
as frozen feature extractors for the JEPA training pipeline. The LLM's
hidden states provide rich semantic embeddings; only a small projection
head (~2-8M params) is trained and distributed via FedAvg. The LLM
weights never leave the node.

New files:
- nodes/common/local_models.py: Model catalog (11 models), host hardware
  probe (RAM, VRAM, disk, MPS), compatibility filtering, recommendation
- nodes/common/llm_backbone.py: LLMBackboneEncoder (frozen LLM + trainable
  projection head), BackboneJEPATrainer (JEPA training with LLM features),
  learned layer mixing, EMA target encoder

Integration:
- ml.py: New backbone training path in train_vljepa_on_task() when
  backbone_model_id is set in task_spec
- text_data.py: Include raw_texts in batch dict for LLM re-tokenization
- training_feed.py: Pass backbone_model_id through to task spec
- config.py: Add backbone_model_id to ModelConfig
- service.py: Wire backbone_model_id from config to training feed,
  fix governance variable reference bug
- ws_server.py: Add local_models and set_local_model WS endpoints
- pyproject.toml: Add transformers + accelerate to [network] deps

Author: EightRice

atn/ws_server.py

@@ -900,6 +900,39 @@ async def _do_oauth() -> None:
             except Exception as e:
                 return {"msg_id": msg_id, "ok": False, "error": str(e)}
 
+        # Local model selection (LLM backbone for JEPA training)
+        if msg_type == "local_models":
+            try:
+                from nodes.common.local_models import host_status
+                return {"msg_id": msg_id, "ok": True, "result": host_status()}
+            except ImportError:
+                return {"msg_id": msg_id, "ok": False, "error": "Network package not installed"}
+            except Exception as e:
+                return {"msg_id": msg_id, "ok": False, "error": str(e)}
+
+        if msg_type == "set_local_model":
+            model_id = msg.get("model_id", "")
+            try:
+                from nodes.common.local_models import get_model_spec
+                if model_id and not get_model_spec(model_id):
+                    return {"msg_id": msg_id, "ok": False, "error": f"Unknown model: {model_id}"}
+                # Update the autonet config
+                if hasattr(self.runtime, "autonet") and self.runtime.autonet:
+                    bridge = self.runtime.autonet
+                    if hasattr(bridge, "_autonet_config") and bridge._autonet_config:
+                        bridge._autonet_config.model.backbone_model_id = model_id
+                    # Update active training feed config
+                    if bridge._service and bridge._service._training_feed:
+                        bridge._service._training_feed.config.backbone_model_id = model_id
+                return {
+                    "msg_id": msg_id, "ok": True,
+                    "result": {"model_id": model_id, "status": "set" if model_id else "cleared"},
+                }
+            except ImportError:
+                return {"msg_id": msg_id, "ok": False, "error": "Network package not installed"}
+            except Exception as e:
+                return {"msg_id": msg_id, "ok": False, "error": str(e)}
+
         # New conversation: reset conversation history without changing model
         if msg_type == "new_conversation":
             await self.runtime.new_conversation()

nodes/common/config.py

@@ -50,14 +50,16 @@ class BlobStoreConfig:
 @dataclass
 class ModelConfig:
     """Model architecture config."""
-    architecture: str = "jepa"  # "simplenet", "jepa", "vl_jepa"
+    architecture: str = "jepa"  # "simplenet", "jepa", "vl_jepa", "backbone"
     image_size: int = 32
     patch_size: int = 4
     embed_dim: int = 192
     num_heads: int = 3
     encoder_depth: int = 6
     predictor_depth: int = 3
     predictor_embed_dim: int = 96
+    # LLM backbone (when architecture="backbone")
+    backbone_model_id: str = ""  # HuggingFace repo ID, e.g. "Qwen/Qwen3-1.7B"
 
 
 @dataclass